• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.390-399
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• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.1-9
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• 2024

Although federated learning is designed to be safer than centralized methods in terms of security and privacy, it still has many vulnerabilities. An attacker performing an adversarial attack intentionally manipulates the deep learning model by injecting carefully crafted input data, that is, adversarial examples, into the client's training data to induce misclassification. A common defense strategy against this is so-called adversarial training, which involves preemptively learning the characteristics of adversarial examples into the model. Existing research assumes a scenario where all clients are under adversarial attack, but considering the number of clients in federated learning is very large, this is far from reality. In this paper, we experimentally examine aspects of adversarial training in a scenario where some of the clients are under attack. Through experiments, we found that there is a trade-off relationship in which the classification accuracy for normal samples decreases as the classification accuracy for adversarial examples increases. In order to effectively utilize this trade-off relationship, we present a method to perform adversarial training by adaptively selecting a loss function depending on whether the client is attacked.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.25-36
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• 2023

Today, as AI (Artificial Intelligence) technology is introduced in various fields, including security, the development of technology is accelerating. However, with the development of AI technology, attack techniques that cleverly bypass malicious behavior detection are also developing. In the classification process of AI models, an Adversarial attack has emerged that induces misclassification and a decrease in reliability through fine adjustment of input values. The attacks that will appear in the future are not new attacks created by an attacker but rather a method of avoiding the detection system by slightly modifying existing attacks, such as Adversarial attacks. Developing a robust model that can respond to these malware variants is necessary. In this paper, we propose two methods of generating Adversarial attacks as efficient Adversarial attack generation techniques for improving Robustness in AI models. The proposed technique is the XAI-based attack technique using the XAI technique and the Reference based attack through the model's decision boundary search. After that, a classification model was constructed through a malicious code dataset to compare performance with the PGD attack, one of the existing Adversarial attacks. In terms of generation speed, XAI-based attack, and reference-based attack take 0.35 seconds and 0.47 seconds, respectively, compared to the existing PGD attack, which takes 20 minutes, showing a very high speed, especially in the case of reference-based attack, 97.7%, which is higher than the existing PGD attack's generation rate of 75.5%. Therefore, the proposed technique enables more efficient Adversarial attacks and is expected to contribute to research to build a robust AI model in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1553-1571
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• 2018

Currently, many of schemes for smart grid data aggregation are based on a one-level gateway (GW) topology. Since the data aggregation granularity in this topology is too single, the control center (CC) is unable to obtain more fine-grained data aggregation results for better monitoring smart grid. To improve this issue, Shen et al. propose an efficient privacy-preserving cube-data aggregation scheme in which the system model consists of two-level GW. However, a risk exists in their scheme that attacker could forge the signature by using leaked signing keys. In this paper, we propose a secure and fine-grained electricity consumption aggregation scheme for smart grid, which employs the homomorphic encryption to implement privacy-preserving aggregation of users' electricity consumption in the two-level GW smart grid. In our scheme, CC can achieve a flexible electricity regulation by obtaining data aggregation results of various granularities. In addition, our scheme uses the forward-secure signature with backward-secure detection (FSBD) technique to ensure the forward-backward secrecy of the signing keys. Security analysis and experimental results demonstrate that the proposed scheme can achieve forward-backward security of user's electricity consumption signature. Compared with related schemes, our scheme is more secure and efficient.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.6
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• pp.348-353
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• 2010

A threshold decryption scheme is a multi-party public key cryptosystem that allows any sufficiently large subset of participants to decrypt a ciphertext, but disallows the decryption otherwise. When performing a threshold decryption, a third party is often involved to guarantee fairness among the participants. To maintain the security of the protocol as high as possible, it is desirable to lower the level of trust and the amount of information given to the third party. In this paper, we present a threshold decryption scheme which allows the anonymity of the participants as well as the fairness by employing a semi-trusted third party (STTP) which follows the protocol properly with the exception that it keeps a record of all its intermediate computations. Our solution preserves the security and fairness of the previous scheme and reveals no information about the identities of the participants and the plaintext even though an attacker is allowed to access the storage of the STTP.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.210-222
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• 2011

In this work, we study the problem of privacy-preservation data publishing in moving objects databases. In particular, the trajectory of a mobile user in a plane is no longer a polyline in a two-dimensional space, instead it is a two-dimensional surface of fixed width $2A_{min}$, where $A_{min}$ defines the semi-diameter of the minimum spatial circular extent that must replace the real location of the mobile user on the XY-plane, in the anonymized (kNN) request. The desired anonymity is not achieved and the entire system becomes vulnerable to attackers, since a malicious attacker can observe that during the time, many of the neighbors' ids change, except for a small number of users. Thus, we reinforce the privacy model by clustering the mobile users according to their motion patterns in (u, ${\theta}$) plane, where u and ${\theta}$ define the velocity measure and the motion direction (angle) respectively. In this case, the anonymized (kNN) request looks up neighbors, who belong to the same cluster with the mobile requester in (u, ${\theta}$) space: Thus, we know that the trajectory of the k-anonymous mobile user is within this surface, but we do not know exactly where. We transform the surface's boundary poly-lines to dual points and we focus on the information distortion introduced by this space translation. We develop a set of efficient spatiotemporal access methods and we experimentally measure the impact of information distortion by comparing the performance results of the same spatiotemporal range queries executed on the original database and on the anonymized one.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.1
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• pp.57-67
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• 2013

Lately, intrusive incidents (including system hacking, viruses, worms, homepage alterations, and data leaks) have not involved the distribution of an virus or worm, but have been designed to acquire private information or trade secrets. Because an attacker uses advanced intelligence and attack techniques that conceal and alter data in a computer, the collector cannot trace the digital evidence of the attack. In an initial incident response first responser deals with the suspect or crime scene data that needs investigative leads quickly, in accordance with forensic process methodology that provides the identification of digital evidence in a systematic approach. In order to an effective initial response to first responders, this paper analyzes the collection data such as user usage profiles, chronology timeline, and internet data according to CFFPM(computer forensics field triage process model), proceeds to design, and implements a collection application to deploy the client/server architecture on the Windows based computer.

• Convergence Security Journal
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• v.11 no.6
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• pp.9-15
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• 2011

It is difficult to identify attack requests from normal ones when those attacks are based on CSRF which enables an attacker transmit fabricated requests of a trusted user to the website. For the protection against the CSRF, there have been a lot of research efforts including secret token, custom header, proxy, policy model, CAPTCHA, and user reauthentication. There remains, however, incapacitating means and CAPTCHA and user reauthentication incur user inconvenience. In this paper, we propose a method to detect CSRF attacks by analyzing the structure of websites and the usage patterns. Potential victim candidates are selected and website usage patterns according to the structure and usage logs are analyzed. CSRF attacks can be detected by identifying normal usage patterns. Also, the proposed method does not damage users' convenience not like CAPTCHA by requiring user intervention only in case of detecting abnormal requests.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.85-98
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• 2010

An advanced and proactive response mechanism against diverse attacks on All-IP network should be proposed for enhancing its security and reliability on open network. There are two main research works related to this study. First one is the SPIE system with hash function on Bloom filter and second one is the Sinkhole routing mechanism using BGP protocol for verifying its transmission path. Therefore, advanced traceback and network management mechanism also should be necessary on All-IP network environments against DDoS attacks. In this study, we studied and proposed a new IP traceback mechanism on All-IP network environments based on existing SPIE and Sinkhole routing model when diverse DDoS attacks would be happen. Proposed mechanism has a Manager module for controlling the regional router with using packet monitoring and filtering mechanism to trace and find the attack packet's real transmission path. Proposed mechanism uses simplified and optimized memory for storing and memorizing the packet's hash value on bloom filter, with which we can find and determine the attacker's real location on open network. Additionally, proposed mechanism provides advanced packet aggregation and monitoring/control module based on existing Sinkhole routing method. Therefore, we can provide an optimized one in All-IP network by combining the strength on existing two mechanisms. And the traceback performance also can be enhanced compared with previously suggested mechanism.

• Journal of Communications and Networks
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• v.16 no.4
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• pp.397-406
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• 2014

Radio frequency (RF) jamming is a denial of service attack targeted at wireless networks. In resource-hungry scenarios with constant traffic demand, jamming can create connectivity problems and seriously affect communication. Therefore, the vulnerabilities of wireless networks must be studied. In this study, we investigate a particular type of RF jamming that exploits the semantics of physical (PHY) and medium access control (MAC) layer protocols. This can be extended to any wireless communication network whose protocol characteristics and operating frequencies are known to the attacker. We propose two efficient jamming techniques: A low-data-rate random jamming and a shot-noise based protocol-aware RF jamming. Both techniques use shot-noise pulses to disrupt ongoing transmission ensuring they are energy efficient, and they significantly reduce the detection probability of the jammer. Further, we derived the tight upper bound on the duration and the number of shot-noise pulses for Wi-Fi, GSM, and WiMax networks. The proposed model takes consider the channel access mechanism employed at the MAC layer, data transmission rate, PHY/MAC layer modulation and channel coding schemes. Moreover, we analyze the effect of different packet sizes on the proposed jamming methodologies. The proposed jamming attack models have been experimentally evaluated for 802.11b networks on an actual testbed environment by transmitting data packets of varying sizes. The achieved results clearly demonstrate a considerable increase in the overall jamming efficiency of the proposed protocol-aware jammer in terms of packet delivery ratio, energy expenditure and detection probabilities over contemporary jamming methods provided in the literature.